Geology topics

Christian Joseph Che-Castaldo

Publications and source records attributed to Christian Joseph Che-Castaldo.

7 recordsLinked to original sources

USGS2026

Space-based monitoring of penguin diet links sea ice, food webs, and population change

Rapid environmental change is reshaping Antarctic ecosystems through shifts in temperature and sea ice dynamics, with implications for species such as the iconic Adélie penguin ( Pygoscelis adeliae ). Because Antarctic predators rely on a relatively small number of key prey resources in the region 1 whose life histories are intrinsically linked to sea ice, 2 , 3 these abiotic changes are expected to cascade through food webs. However, the scale and remoteness of Antarctica have limited efforts to link environmental change to ecological responses across the continent. Combining tools from imaging spectroscopy, stable isotope analysis, and hierarchical statistical modeling, we reconstructed Adélie penguin diet across the entirety of the species’ global range over a three-decade period (1984–2013) using satellite imagery from the Landsat program. This approach leveraged the distinct spectral properties (a generalized measure of color) of penguin guano as observed by satellites, 4 , 5 which vary according to penguin diet. 6 , 7 We found pronounced differences in diet across the continent and that year-to-year dietary shifts were strongly associated with sea ice dynamics. Higher sea ice corresponded to more fish-based diets, whereas lower sea ice was associated with a greater reliance on krill. Additionally, spatial differences in penguin diet were associated with long-term trends in penguin abundance, linking food web processes to large-scale population dynamics. This study represents the first use of satellite observations to capture trophic dynamics at continental and decadal scales and highlights how environmental change might restructure Antarctic food webs and influence the future of a key sentinel species.

Current Biology↗

USGS2026

An Antarctic ecosystem value index to quantify ecological value across trophic levels and over time

The Southern Ocean around Antarctica is one of the fastest changing regions on the planet and an emerging resource frontier for fisheries. Here, we present the Antarctic Ecosystem Value Index created by merging ecosystem information across food web trophic levels, from phytoplankton to fish and penguins, to quantify the ecological value of marine areas around the Antarctic continent. We find that coastal polynyas - areas of reduced sea-ice - have Index values 31–72% higher than surrounding areas, suggesting that these areas are biologically valuable hot spots for a number of ice-dependent Antarctic Species. Using output from an Earth system model to generate future projections of the Index, we find that high-value locations, often within polynyas, are likely to continue to be valuable throughout the 21st century despite environmental changes. The Antarctic Ecosystem Value Index indicates that penguins lose importance as their habitat becomes increasingly unsuitable, so protecting high-value habitat areas may be critical for these species. This study also shows that while many high-value Index areas are within existing or proposed Marine Protected Areas, there are several opportunities for adopting additional protection, particularly in East Antarctica and the Amundsen Sea.

Nature Communications↗

USGS2025

Living with uncertainty: Using multi-model large ensembles to assess emperor penguin extinction risk for the IUCN Red List

Improved methods for identifying species at risk are needed to strengthen climate change vulnerability assessments, as current estimates indicate that up to one million species face extinction due to environmental changes. Integrating multiple sources of uncertainty enhances the robustness of Red List of Threatened Species assessments, providing a more comprehensive understanding of species’ risks. We present a comprehensive framework that incorporates uncertainties, including measurement error, structural uncertainty, natural variability, future climate emissions scenario, and extreme events of sea ice loss, to evaluate the extinction risk of the emperor penguin ( Aptenodytes forsteri ), currently classified as Near-Threatened. We apply three ecological models, one bioclimatic and two metapopulation models, combined with a multi-model large ensemble (MMLE) of climate projections from general circulation models, to conduct a Red List evaluation at both global, regional and colony levels. Our results show that emperor penguins could be classified under a range of Red List categories depending on the ecological model, Intergovernmental Panel on Climate Change (IPCC) climate emissions scenario, and extreme event frequency. Under Criterion A, global classifications vary from Vulnerable to Critically Endangered. Severe declines are projected in the Indian and East Pacific sectors, Dronning Maud Land and the Amundsen-Bellingshausen Sea, with Criterion E indicating that 24% to 100% of colonies meet Endangered status thresholds, depending on huddling thresholds and ecological models. This study represents the first application of an MMLE coupled with an ecological ensemble approach to project climate change impacts on a species, capturing a comprehensive range of uncertainties and offering a framework for improving forecasting and decision-making under climate change.

Biological Conservation↗

USGS2025

Temporal and spatial equivalence in demographic responses of emperor penguins (Aptenodytes forsteri) to environmental change

1. Population ecology and biogeography applications often necessitate the transfer of models across spatial and/or temporal dimensions to make predictions outside the bounds of the data used for model fitting. However, ecological data are often spatiotemporally unbalanced such that the spatial or the temporal dimension tends to contain more data than the other. This unbalance frequently leads model transfers to become substitutions, which are predictions to a different dimension than the predictive model was built on. Despite the prevalence of substitutions in ecology, studies validating their performance and their underlying assumptions are scarce. 2. Here, we present a successful case study demonstrating both space-for-time and time-for-space substitutions using emperor penguins (Aptenodytes forsteri) as the focal species. Using abundance-based species distribution models (aSDM) of adult emperor penguins in attendance during spring across 50 colonies, we predict long-term annual fluctuations in fledgling abundance and breeding success at a single colony, Pointe Géologie. Subsequently, we construct statistical models from time series of extended counts on Pointe Géologie to predict average fledgling abundance across 50 colonies. 3. Our analysis reveals that distance to nearest open water (NOW) exhibits the strongest association with both temporal and spatial data. aSDM’s space-for-time substitution performance, as measured by Pearson correlation coefficient was 0.63 and 0.56 when predicting breeding success and fledgling abundance time series, respectively. Linear regression of fledgling abundance on NOW yields similar time-for-space substitution performance when predicting abundance distribution of emperor penguin colonies with a correlation coefficient of 0.58. 4. We posit that such space-time equivalence arises because: 1) emperor penguins colonies conform to their existing fundamental niche; 2) there is not yet any environmental novelty when comparing the spatial vs temporal variation of distance to nearest open water; and 3) models of more specific components of life histories, such as fledgling abundance, rather than occurrence or total population abundance, are more transferable. Identifying these conditions empirically can enhance the qualitative validation of substitutions in cases where direct validation data are lacking.

Journal of Animal Ecology↗

USGS2024

Penguin colony georegistration using camera pose estimation and phototourism

Satellite-based remote sensing and uncrewed aerial imagery play increasingly important roles in the mapping of wildlife populations and wildlife habitat, but the availability of imagery has been limited in remote areas. At the same time, ecotourism is a rapidly growing industry and can yield a vast catalog of photographs that could be harnessed for monitoring purposes, but the inherently ad-hoc and unstructured nature of these images make them difficult to use. To help address this, a subfield of computer vision known as phototourism has been developed to leverage a diverse collection of unstructured photographs to reconstruct a georeferenced three-dimensional scene capturing the environment at that location. Here we demonstrate the use of phototourism in an application involving Antarctic penguins, sentinel species whose dynamics are closely tracked as a measure of ecosystem functioning, and introduce a semi-automated pipeline for aligning and registering ground photographs using a digital elevation model (DEM) and satellite imagery. We employ the Segment Anything Model (SAM) for the interactive identification and segmentation of penguin colonies in these photographs. By creating a textured 3D mesh from the DEM and satellite imagery, we estimate camera poses to align ground photographs with the mesh and register the segmented penguin colony area to the mesh, achieving a detailed representation of the colony. Our approach has demonstrated promising performance, though challenges persist due to variations in image quality and the dynamic nature of natural landscapes. Nevertheless, our method offers a straightforward and effective tool for the georegistration of ad-hoc photographs in natural landscapes, with additional applications such as monitoring glacial retreat.

PLoS ONE↗

USGS2024

The potential for species distribution models to distinguish source populations from sinks

1. While species distribution models (SDM) are frequently used to predict species occurrences to help inform conservation management, there is limited evidence evaluating whether habitat suitability can reliably predict intrinsic growth rates or distinguish source from sink populations. Filling this knowledge gap is critical for conservation science, as applications of SDMs for management purposes ultimately depend on these typically unobserved population or metapopulation dynamics. 2. Using regression, we associate previously published population level estimates of intrinsic growth and abundance derived from a Bayesian analysis of mark-recapture data for 17 bird species found in the contiguous United States with SDM habitat suitability estimates fitted here to opportunistic data for these same species. We then use AUC to measure how well SDMs can distinguish populations categorized as sources and sinks, depending on their intrinsic growth rates estimated from the mark-recapture data. We built SDMs using two different approaches, boosted regression trees (BRT) and Generalized Linear Models (GLM), and compared their predictive performance. Each SDM was built with presence points obtained from eBird and 10 environmental variables previously selected to model intrinsic growth rates and abundance for these species. 3. We show that SDMs built with opportunistic data are poor predictors of species demography in general; both BRT and GLM explained very little spatial variation of intrinsic growth rate and population abundance (median R2 across 17 species was close to 0.1 for both SDM methods). SDMs do, however, estimate higher suitability for source populations as compared to sinks. Out of 13 species which had both source and sink populations, both BRT and GLM had AUC values greater than 0.7 for 7 species when discriminating between sources and sinks. 4. Habitat suitability have the potential to be a useful measure to indicate a population’s ability to sustain itself as a source population, however more research on a diverse set of taxa is essential to fully explore this potential. This interpretation of habitat suitability can be particularly useful for conservation practice, and identification of explicit cases of when and how SDMs fail to match population demography can be informative for advancing ecological theory.

Journal of Animal Ecology↗

USGS2024

Detecting stochasticity in population time series using a non-parametric test of intrinsic predictability

1. Many ecological systems dominated by stochastic dynamics can produce complex time series that inherently limit forecast accuracy. The ‘intrinsic predictability’ of these systems can be approximated by a time series complexity metric called weighted permutation entropy (WPE). While WPE is a useful metric to gauge forecast performance prior to model building, it is sensitive to noise and may be biased depending on the length of the time series. Here, we introduce a simple randomized permutation test (rWPE) to assess whether a time series is intrinsically more predictable than white noise. 2. We apply rWPE to both simulated and empirical data to assess its performance and usefulness. To do this, we simulate population dynamics under various scenarios, including a linear trend, chaotic, periodic and equilibrium dynamics. We further test this approach with observed abundance time series for 932 species across four orders of animals from the Global Population Dynamics Database. Finally, using Adélie ( Pygoscelis adeliae ) and emperor penguin ( Aptenodytes forsteri ) time series as case studies, we demonstrate the application of rWPE to multiple populations for a single species. 3. We show that rWPE can determine whether a system is significantly more predictable than white noise, even with time series as short as 10 years that show an apparent trend under biologically realistic stochasticity levels. Additionally, rWPE has statistical power close to 100% when time series are at least 30 time steps long and show chaotic or periodic dynamics. Power decreases to ~10% under equilibrium dynamics, irrespective of time series length. Among four classes of animal taxa, mammals have the highest relative frequency (28%) of time series that are both longer than 30 time steps and indistinguishable from white noise in terms of complexity, followed by insects (16%), birds (16%) and bony fishes (11%). 4. rWPE is a straightforward and useful method widely applicable to any time series, including short ones. By informing forecasters of the inherent limitations to a system's predictability, it can guide a modeller's expectations for forecast performance.

Methods in Ecology and Evolution↗